Image classification using separable invariants moments based on Racah polynomials

The main purpose of this work is to present a comparison between the classical discrete moment invariants and separable discrete moment invariants based on Racah polynomials in terms 2D object recognition accuracy. For that, we present a pattern recognition system based on separable moment invariants and using the 1-NN (k-Nearest Neighbor) as method of classification. The results show that the separable discrete moment invariants express significant improvement in terms of recognition accuracy and invariability.